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Elevate your AI, rendering, and simulations with Blendr

For AI, rendering, and simulations, Blendr offers high-performance computing sans centralized costs and scalability limits. Optimize resource usage, minimize expenses, and maintain uninterrupted productivity.
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  • BCM

    Decentralized GPU
    Compute Mesh (BCM)

    Blendr's BCM effectively aggregates underutilized GPU resources across a global network. This network optimally distributes computational tasks, ensuring each GPU's capacity is utilized efficiently, thereby providing a scalable solution for demanding rendering tasks and Al computations without the overhead associated with traditional cloud computing services.
  • DARL

    Blockchain-Enabled Digital Rights Management (DARL)

    Blendr incorporates granular digital rights management through its DARL system. This leverages the immutability and transparency of blockchain to offer on-chain traceability for digital assets, ensuring creators maintain control over their work and receive fair compensation.
Coming Soon

Blendr
Box X1

A cutting-edge, plug-and-play solution that allows users to host a $BLENDR node at home, enhancing the decentralized GPU network and offering passive income opportunities. Compact and powerful, it's your gateway to advanced rendering and AI computing.

Blendr Box X1 Specifications

  • Equipped with RTX 4060* for optimized processing in complex tasks.
  • M.2 SSD support ensures rapid data handling.
  • Thunderbolt™ 4 for speedy transfers up to 40Gbps and connecting high-performance devices.
  • TPM 2.0 for enhanced data security.
  • Compliant with CE, FCC, KC, RoHS standards.
  • Ultra-compact 3.5” form factor for space efficiency.
*Specifications, including the GPU, are based on our current prototype and might change to optimize performance.

Join our GPU sharing network and earn $BLENDR tokens

for powering rendering and computational tasks
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MVP Development Roadmap

Establish a functional prototype that allows GPU owners to offer their computing power and users to execute tasks using available GPU resources.

Phase 1

Initial MVP Setup

Client-Side Python Program (CLI)

Purpose:

Enables GPU owners to register their devices with the central server and listen for incoming tasks.

Features:

  • Detect local GPU resources.
  • Establish WebSocket connections to the central server for real-time communication.
  • Receive task details and execute tasks using the GPU.

Central Server

Technology:

Enables GPU owners to register their devices with the central server and listen for incoming tasks.

Features:

  • Manage user registrations and authentications.
  • Handle task dispatching and monitor GPU availability.
  • Provide a WebSocket endpoint for client communications.

Web Dashboard

Purpose:

Allows users to view available GPU resources and submit tasks.

Features:

  • Display overall GPU capacity or specific available GPUs (configurable based on feedback).
  • User interface for submitting tasks and viewing task status.

Data Management with AWS S3

Implementation:

Utilize S3 for storing input datasets and processed results.

Features:

  • Generate and manage S3 access keys to allow users direct access to upload and download data, reducing load on the central server.

Phase 2

Scale and Decentralize

Decentralized Storage System

Purpose:

Replace S3 with a decentralized storage solution to enhance privacy and reduce reliance on centralized services.

Plan:

  • Research and integrate technologies like IPFS (InterPlanetary File System) or similar for storing and retrieving task data.

Token-Based Economy

Purpose:

Introduce a blockchain-based token system to reward users based on the GPU power they provide.

Implementation:

  • Develop and deploy smart contracts on a blockchain platform.
  • Implement a system for tracking resource usage and issuing tokens accordingly.

Phase 3

Advanced GPU Integration

Distributed GPU Processing

Goal:

Enable multiple GPUs to work together on complex tasks, enhancing processing capabilities and efficiency.

Challenges:

Address synchronization, data consistency, and network latency.

Approach:

Utilize advanced distributed computing frameworks and modify the client software to support distributed tasks.

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